Libraries:
library(tidyverse)
library(readr)
library(rvest)
library(stats)
library(readxl)
library(dplyr)
library(stringr)
library(ggplot2)
library(ggthemes)
library(stringr)
library(data.table)
library(geojsonio)
library(leaflet)
library(rgdal)
library(haven)
library(stargazer)
Data:
Model 3:
First making a graph of annual GDP highs and lows – maybe a temporary proxy for recessions?
Annual enrollment graph:
Join enrollment data and gdp data to create linear model test:
Graph it?
College Proximity Question 5/3: (Reading in Ivy’s data)
cz <- read_dta('cz.dta')
Error in read_dta("cz.dta") : could not find function "read_dta"
Read in/create mobility data: (Trends in Mobility: Commuting Zone Intergenerational Mobility Estimates by Birth Cohort) https://opportunityinsights.org/data/?geographic_level=101&topic=0&paper_id=0#resource-listing
Read in geojson file:
Commuting zones on the map (cz.geojson@data) are in 1990s format. They need to be converted so our post-2000 data can be connected to the shapefiles: (https://www.ers.usda.gov/data-products/commuting-zones-and-labor-market-areas/)
Try to run some lms:
Ivy’s STATA code:
foreach x of varlist ncollege nfouryr nfouryrpriv npub nelite hascollege{
foreach y of varlist kfr_pooled_pooled_p1 kfr_pooled_pooled_p25 kfr_pooled_pooled_p50 kfr_pooled_pooled_p75 kfr_pooled_pooled_p100 {
reg `y' `x' popdensity2010 med_hhinc2016, r
outreg2 using `x'_kfr, excel append ctitle(`y')
}
Variables of interest:
as.formula(paste0(yvar1, " ~ ", paste0(xvars1, collapse = " + ")))
kfr_pooled_pooled_p1 ~ ncollege + nfouryr + nfouryrpriv + npub +
nelite + hascollege + popdensity2010 + med_hhinc2016
testing?
stargazer(lm.kfr_p1,
type = "text",
dep.var.labels = c("kfr_pooled_pooled_p1"))
length of NULL cannot be changedlength of NULL cannot be changedlength of NULL cannot be changedlength of NULL cannot be changedlength of NULL cannot be changed
===============================================
Dependent variable:
---------------------------
kfr
-----------------------------------------------
ncollege 0.003
(0.004)
nfouryr 0.007*
(0.004)
nfouryrpriv -0.012***
(0.004)
npub -0.011***
(0.004)
nelite 0.006
(0.005)
hascollege -0.049***
(0.006)
popdensity2010 -0.0001***
(0.00001)
med_hhinc2016 0.00000***
(0.00000)
Constant 0.251***
(0.012)
-----------------------------------------------
Observations 741
R2 0.281
Adjusted R2 0.273
Residual Std. Error 0.062 (df = 732)
F Statistic 35.756*** (df = 8; 732)
===============================================
Note: *p<0.1; **p<0.05; ***p<0.01
nelite on kfr at different levels
htmtable.nelite <- stargazer(nelite.p1, nelite.p25, nelite.p50, nelite.p75, nelite.p100,
type = "html",
dep.var.labels = c("Bottom 1%", "25%", "50%", "75%", "Top 1%"),
out = "nelitetable.html")
length of NULL cannot be changedlength of NULL cannot be changedlength of NULL cannot be changedlength of NULL cannot be changedlength of NULL cannot be changed
<table style="text-align:center"><tr><td colspan="6" style="border-bottom: 1px solid black"></td></tr><tr><td style="text-align:left"></td><td colspan="5"><em>Dependent variable:</em></td></tr>
<tr><td></td><td colspan="5" style="border-bottom: 1px solid black"></td></tr>
<tr><td style="text-align:left"></td><td>Bottom 1%</td><td>25%</td><td>50%</td><td>75%</td><td>Top 1%</td></tr>
<tr><td style="text-align:left"></td><td>(1)</td><td>(2)</td><td>(3)</td><td>(4)</td><td>(5)</td></tr>
<tr><td colspan="6" style="border-bottom: 1px solid black"></td></tr><tr><td style="text-align:left">nelite</td><td>-0.004</td><td>-0.002</td><td>-0.001</td><td>0.001</td><td>0.004</td></tr>
<tr><td style="text-align:left"></td><td>(0.003)</td><td>(0.003)</td><td>(0.002)</td><td>(0.002)</td><td>(0.003)</td></tr>
<tr><td style="text-align:left"></td><td></td><td></td><td></td><td></td><td></td></tr>
<tr><td style="text-align:left">popdensity2010</td><td>-0.0001<sup>***</sup></td><td>-0.0001<sup>***</sup></td><td>-0.00004<sup>***</sup></td><td>-0.00002<sup>***</sup></td><td>-0.00000</td></tr>
<tr><td style="text-align:left"></td><td>(0.00001)</td><td>(0.00001)</td><td>(0.00001)</td><td>(0.00001)</td><td>(0.00001)</td></tr>
<tr><td style="text-align:left"></td><td></td><td></td><td></td><td></td><td></td></tr>
<tr><td style="text-align:left">med_hhinc2016</td><td>0.00000<sup>***</sup></td><td>0.00000<sup>***</sup></td><td>0.00000<sup>***</sup></td><td>0.00000</td><td>-0.00000<sup>***</sup></td></tr>
<tr><td style="text-align:left"></td><td>(0.00000)</td><td>(0.00000)</td><td>(0.00000)</td><td>(0.00000)</td><td>(0.00000)</td></tr>
<tr><td style="text-align:left"></td><td></td><td></td><td></td><td></td><td></td></tr>
<tr><td style="text-align:left">Constant</td><td>0.221<sup>***</sup></td><td>0.350<sup>***</sup></td><td>0.471<sup>***</sup></td><td>0.585<sup>***</sup></td><td>0.770<sup>***</sup></td></tr>
<tr><td style="text-align:left"></td><td>(0.012)</td><td>(0.010)</td><td>(0.009)</td><td>(0.009)</td><td>(0.009)</td></tr>
<tr><td style="text-align:left"></td><td></td><td></td><td></td><td></td><td></td></tr>
<tr><td colspan="6" style="border-bottom: 1px solid black"></td></tr><tr><td style="text-align:left">Observations</td><td>741</td><td>741</td><td>741</td><td>741</td><td>741</td></tr>
<tr><td style="text-align:left">R<sup>2</sup></td><td>0.141</td><td>0.104</td><td>0.055</td><td>0.016</td><td>0.030</td></tr>
<tr><td style="text-align:left">Adjusted R<sup>2</sup></td><td>0.137</td><td>0.100</td><td>0.051</td><td>0.012</td><td>0.026</td></tr>
<tr><td style="text-align:left">Residual Std. Error (df = 737)</td><td>0.068</td><td>0.057</td><td>0.050</td><td>0.047</td><td>0.053</td></tr>
<tr><td style="text-align:left">F Statistic (df = 3; 737)</td><td>40.261<sup>***</sup></td><td>28.387<sup>***</sup></td><td>14.380<sup>***</sup></td><td>4.096<sup>***</sup></td><td>7.651<sup>***</sup></td></tr>
<tr><td colspan="6" style="border-bottom: 1px solid black"></td></tr><tr><td style="text-align:left"><em>Note:</em></td><td colspan="5" style="text-align:right"><sup>*</sup>p<0.1; <sup>**</sup>p<0.05; <sup>***</sup>p<0.01</td></tr>
</table>
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---
title: "econ401 final project R Notebook"
output: html_notebook
---

Libraries:
```{r libraries, results = "markup"}
library(tidyverse)
library(readr)
library(rvest)
library(stats)
library(readxl)
library(dplyr)
library(stringr)
library(ggplot2)
library(ggthemes)
library(stringr)
library(data.table)
library(geojsonio)
library(leaflet)
library(rgdal)
library(haven)
library(stargazer)
```

Data:
```{r data, include = FALSE}
# GDP up to Feb 2020
# https://ihsmarkit.com/products/us-monthly-gdp-index.html
gdp.index.data <- readxl::read_xlsx('US-Monthly-GDP-History-Data.xlsx', sheet = 3)
gdp.index <- gdp.index.data
colnames(gdp.index)[1] <- "Y_M"
year.month <- str_split_fixed(gdp.index$Y_M, ' - ', 2)
colnames(year.month) <- c('Year', 'Month')
gdp.index <- cbind(year.month, gdp.index[, -1])
gdp.annual <- gdp.index %>%
  group_by(Year) %>%
  summarize(MaxGDP = max(`Monthly Real GDP Index`),
            MinGDP = min(`Monthly Real GDP Index`))

# https://nces.ed.gov/programs/digest/d18/tables/dt18_306.10.asp
enrollment.data <- read_xls('tabn306.10.xls')
enrollment <- enrollment.data[1:12]
# enrollment is in thousands
enrollment <- enrollment[-c(1, 3, 15, 27, 39, 51, 63, 75, 99, 111, 123, 135:139), ]
col1 <- data.frame(str_remove_all(enrollment[[1]], '\\.'), stringsAsFactors = FALSE)
col1[2, 1] <- "All_Students"
enrollment <- cbind(col1, enrollment[, -1])
enrollment <- t(enrollment)
rownames(enrollment) <- c()
colnames(enrollment) <- enrollment[1, ]
enrollment <- data.frame(enrollment)
colnames(enrollment)[1] <- 'Year'
enrollment <- enrollment[-1, ]
Years <- as.numeric(str_extract(enrollment$Year, "[:digit:]{4}"))
enrollment <- cbind(Years, enrollment[, -1])
enrollment <- data.frame(lapply(enrollment, function(x){ 
  gsub("---", NA, x)
}))
str(enrollment)

enrollment1 <- enrollment[, 1:2]
gdp.annual1 <- gdp.annual
all.students <- as.numeric(enrollment[4:11, 2])

gdp.annual$Year <- as.factor(gdp.annual$Year)
enrollment1$Years <- as.factor(enrollment1$Years)
enrollment1$All_Students <- as.numeric(as.character(enrollment1$All_Students))
```

Model 3:

  First making a graph of annual GDP highs and lows -- maybe a temporary proxy for recessions?
```{r graph1, include = FALSE}
gdp.annual %>%
  ggplot() +
  geom_line(mapping = aes(x = Year,
                 y = MaxGDP,
                 group = 1)) +
  geom_line(mapping = aes(x = Year,
                          y = MinGDP,
                          group = 1)) +
  theme_economist() +
  ylab('Real GDP')
```

  Annual enrollment graph:
```{r graph2, include = FALSE}
enrollment1 %>%
  ggplot() +
  geom_line(mapping = aes(x = Years,
                          y = All_Students,
                          group = 1)) +
  theme_economist() +
  ylab('Enrollment')
```

  Join enrollment data and gdp data to create linear model test:
```{r lm1, include = FALSE}
test <- inner_join(enrollment1, gdp.annual1,
          by = c("Years" = "Year"))

lm1 <- lm(All_Students ~ MaxGDP,
          data = test,
          na.action = na.omit)
summary(lm1)
```

  Graph it?
```{r graph3_4, include = FALSE}
test %>%
  ggplot() +
  geom_line(aes(x = Years,
                 y = All_Students,
                group = 1)) +
  theme_economist() +
  ylab('Enrollment by All Students')

test %>%
  ggplot() +
  geom_line(aes(x = Years,
                 y = MaxGDP,
                 group = 1)) +
  # geom_abline(slope = 0.8015, intercept = 6316.7207) +
  theme_economist()
```

College Proximity Question 5/3:
(Reading in Ivy's data)
```{r read proximity data, results = "markup"}
# cz_college <- read_dta("cz_college.dta")
cz <- read_dta('cz.dta')
# colleges <- read_dta('colleges.dta')
# mobility.results <- read_xlsx('mobility_results.xlsx')
```

Read in/create mobility data:
(Trends in Mobility: Commuting Zone Intergenerational Mobility Estimates by Birth Cohort)
https://opportunityinsights.org/data/?geographic_level=101&topic=0&paper_id=0#resource-listing
```{r mobility data, include=FALSE}
# mobility.data <- read_xls('onlinedata1_trends.xls')
# colnames(mobility.data) <- mobility.data[15, ]
# mobility <- mobility.data[-c(1:16), ]
# mobility.1986 <- mobility %>%
#   filter(`Birth Cohort` == 1986)
# mobility.1986$`Commuting Zone` <- as.numeric(mobility.1986$`Commuting Zone`)
# cz.mobility.data <- full_join(mobility.1986[, c(1, 3:8)],
#                   cz,
#                   by = c(`Commuting Zone` = 'cz'))
# 
# cz.mobility <- cz.mobility.data[, c(1:8, 2132:2137)]
# cz.mobility <- cz.mobility[, c(1, 8, 9:14, 3:7, 2)]
# write_csv(cz.mobility, 'cz.mobility.csv')
cz.mobility <- read_csv('cz.mobility.csv')
```

Read in geojson file:
```{r geojson}
cz.geojson <- geojson_read("cz1990.json",
                        what = "sp")
# View(cz.geojson@data)
# cz.geojson %>%
#   leaflet() %>%
#   #addTiles() %>%
#   addPolygons() %>%
#   setView(-96, 37.8, 3)
```

Commuting zones on the map (cz.geojson@data) are in 1990s format. They need to be converted so our post-2000 data can be connected to the shapefiles:
(https://www.ers.usda.gov/data-products/commuting-zones-and-labor-market-areas/)
```{r cz shape combine, include = FALSE}
cz.conversions <- read_xls('cz00_eqv_v1.xls')
cz.conversions <- cz.conversions[, c(2:4)]
cz.conversions$`Commuting Zone ID, 1990` <- as.numeric(cz.conversions$`Commuting Zone ID, 1990`)
cz.conversions$`Commuting Zone ID, 1980` <- as.numeric(cz.conversions$`Commuting Zone ID, 1980`)
colnames(cz.conversions)[2] <- 'cz1990'
colnames(cz.conversions)[1] <- 'cz2000'
colnames(cz.conversions)[3] <- 'cz1980'

head(cz.geojson@data)
cz.geo <- cz.geojson
colnames(cz.mobility)[1] <- 'cz1990'

cz.geo@data <- full_join(cz.geo@data,
                  cz.conversions[, -3],
                  by = c('cz' = 'cz1990'))
cz.geo@data <- left_join(cz.geo@data,
                         cz.mobility,
                         by = c('cz' = 'cz1990'))

cz.geo %>%
  leaflet() %>%
  addPolygons() %>%
  setView(-96, 37.8, 3)
```

Try to run some lms:

  Ivy's STATA code:

    foreach x of varlist ncollege nfouryr nfouryrpriv npub nelite hascollege{
    	
    foreach y of varlist kfr_pooled_pooled_p1 kfr_pooled_pooled_p25 kfr_pooled_pooled_p50 kfr_pooled_pooled_p75 kfr_pooled_pooled_p100 {
    	reg `y' `x' popdensity2010 med_hhinc2016, r 
    	outreg2 using `x'_kfr, excel append ctitle(`y')
    }
    
  Variables of interest:
  
```{r}
yvar1 <- "kfr_pooled_pooled_p1"
xvars1 <- c("ncollege", "nfouryr", "nfouryrpriv", "npub", "nelite", "hascollege", "popdensity2010", "med_hhinc2016")
cz1 <- cz[, c(yvar1, xvars1)]

lm.model1 <- as.formula(paste0(yvar1, " ~ ", paste0(xvars1, collapse =  " + ")))

```

testing?
```{r}
lm.kfr_p1 <- lm(lm.model1,
                data = cz)
summary(lm.kfr_p1)

stargazer(cz1, type = "text", title="Descriptive statistics", digits=1, out="table1.txt")

stargazer(lm.kfr_p1,
          type = "text",
          dep.var.labels = c("kfr_pooled_pooled_p1"))
 #          ,
 #          covariate.labels = c("Gross horsepower", "Rear axle ratio","Four foward gears",
 # "Five forward gears","Type of transmission (manual=1)"), out="models.txt")


```

nelite on kfr at different levels
```{r}
yvar.p1 <- "kfr_pooled_pooled_p1"
yvar.p25 <- "kfr_pooled_pooled_p25"
yvar.p50 <- "kfr_pooled_pooled_p50"
yvar.p75 <- "kfr_pooled_pooled_p75"
yvar.p100 <- "kfr_pooled_pooled_p100"
xvars.nelite <- c("nelite", "popdensity2010", "med_hhinc2016")
lm.nelite.p1 <- as.formula(paste0(yvar.p1, " ~ ", paste0(xvars.nelite, collapse =  " + ")))
lm.nelite.p25 <- as.formula(paste0(yvar.p25, " ~ ", paste0(xvars.nelite, collapse =  " + ")))
lm.nelite.p50 <- as.formula(paste0(yvar.p50, " ~ ", paste0(xvars.nelite, collapse =  " + ")))
lm.nelite.p75 <- as.formula(paste0(yvar.p75, " ~ ", paste0(xvars.nelite, collapse =  " + ")))
lm.nelite.p100 <- as.formula(paste0(yvar.p100, " ~ ", paste0(xvars.nelite, collapse =  " + ")))
nelite.p1 <- lm(lm.nelite.p1, cz)
nelite.p25 <- lm(lm.nelite.p25, cz)
nelite.p50 <- lm(lm.nelite.p50, cz)
nelite.p75 <- lm(lm.nelite.p75, cz)
nelite.p100 <- lm(lm.nelite.p100, cz)
txttable.nelite <- stargazer(nelite.p1, nelite.p25, nelite.p50, nelite.p75, nelite.p100,
          type = "text",
          title = "The Effect of Elite Colleges in Commuting Zone on the Probability that a Child from the 20th Percentile Falls in Each Income Percentile as an Adult",
          dep.var.caption = "Parent Income Percentile",
          dep.var.labels = c("Bottom 1%", "25%", "50%", "75%", "Top 1%"),
          # notes = "Where nelite is the number of elite colleges in commuting zone (cz), popdensity2010 is the cz's popultion density, and med_hhinc2016 is the median household income in cz in 2016.",
          # notes.append = TRUE,
          # notes.align = "l",
          out = "nelitetable.txt")
htmtable.nelite <- stargazer(nelite.p1, nelite.p25, nelite.p50, nelite.p75, nelite.p100,
          type = "html",
          dep.var.labels = c("Bottom 1%", "25%", "50%", "75%", "Top 1%"),
          out = "nelitetable.html")
summary(lm.nelite.p1)
```



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The preview shows you a rendered HTML copy of the contents of the editor. Consequently, unlike *Knit*, *Preview* does not run any R code chunks. Instead, the output of the chunk when it was last run in the editor is displayed.
